Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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Recovery of 3D body pose is a fundamental problem for human motion analysis in many applications such as motion capture, vision interface, visual surveillance, and gesture recognition. In this paper, we present a new image-based approach to infer 3D human structure parameters from uncalibrated video. The estimation is example based. First, we acquire a special motion database through an off-line motion capture process. Second, given uncalibrated motion video, we abstract the extrinsic parameters and then silhouettes database associated with 3D poses is built by projecting each data of the 3D motion database into 2D plane with the extrinsic parameters. Next, with the image silhouettes abstracted from video, the unknown structure parameters are inferred by performs a similarity search in the database of silhouettes using approach based on shape matching. That is, the 3D structure parameters whose 2D projective silhouette is the most similar to the 2D image silhouette are took as the 3D reconstruction structure. We use trampoline sport motion, an example of complex human motion, to demonstrate the effectiveness of our approach.